Morreale, L;
Aigerman, N;
Guerrero, P;
Kim, VG;
Mitra, NJ;
(2022)
Neural Convolutional Surfaces.
In:
Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
(pp. pp. 19311-19320).
Institute of Electrical and Electronics Engineers (IEEE)
Preview |
Text
Morreale_Neural_Convolutional_Surfaces_CVPR_2022_paper.pdf - Accepted Version Download (8MB) | Preview |
Abstract
This work is concerned with a representation of shapes that disentangles fine, local and possibly repeating geometry, from global, coarse structures. Achieving such disentanglement leads to two unrelated advantages: i) a significant compression in the number of parameters required to represent a given geometry; ii) the ability to manipulate either global geometry, or local details, without harming the other. At the core of our approach lies a novel pipeline and neural architecture, which are optimized to represent one specific atlas, representing one 3D surface. Our pipeline and architecture are designed so that disentanglement of global geometry from local details is accomplished through optimization, in a completely unsupervised manner. We show that this approach achieves better neural shape compression than the state of the art, as well as enabling manipulation and transfer of shape details. Project page http://geometry.cs.ucl.ac.uk/projects/2022/cnnmaps/.
| Type: | Proceedings paper |
|---|---|
| Title: | Neural Convolutional Surfaces |
| Event: | 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) |
| Location: | Orleans, LA, USA |
| Dates: | 18th-24th June 2022 |
| ISBN-13: | 978-1-6654-6946-3 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/CVPR52688.2022.01873 |
| Publisher version: | https://doi.org/10.1109/CVPR52688.2022.01873 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10162184 |
Archive Staff Only
 |
View Item |
